Saying “no” to temptation: Want-to motivation improves self-regulation by reducing temptation rather than by increasing self-control.
Bibliographic record
Abstract
Self-regulation has been conceptualized as the interplay between controlled and impulsive processes; however, most research has focused on the controlled side (i.e., effortful self-control). The present studies focus on the effects of motivation on impulsive processes, including automatic preferences for goal-disruptive stimuli and subjective reports of temptations and obstacles, contrasting them with effects on controlled processes. This is done by examining people's implicit affective reactions in the face of goal-disruptive "temptations" (Studies 1 and 2), subjective reports of obstacles (Studies 2 and 3) and expended effort (Study 3), as well as experiences of desires and self-control in real-time using experience sampling (Study 4). Across these multiple methods, results show that want-to motivation results in decreased impulsive attraction to goal-disruptive temptations and is related to encountering fewer obstacles in the process of goal pursuit. This, in turn, explains why want-to goals are more likely to be attained. Have-to motivation, on the other hand, was unrelated to people's automatic reactions to temptation cues but related to greater subjective perceptions of obstacles and tempting desires. The discussion focuses on the implications of these findings for self-regulation and motivation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".